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60,000 Times Slower Python
Manage episode 468099614 series 3610932
المحتوى المقدم من Pragmatic AI Labs and Noah Gift. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Pragmatic AI Labs and Noah Gift أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.
The End of Moore's Law and the Future of Computing Performance
The Automobile Industry Parallel
- 1960s: Focus on power over efficiency (muscle cars, gas guzzlers)
- Evolution through Japanese efficiency, turbocharging, to electric vehicles
- Similar pattern now happening in computing
The Python Performance Crisis
- Matrix multiplication example: 7 hours vs 0.5 seconds
- 60,000x performance difference through optimization
- Demonstrates massive inefficiencies in modern languages
- Industry was misled by Moore's Law into deprioritizing performance
Performance Improvement Hierarchy
Language Choice Improvements:
- Java: 11x faster than Python
- C: 50x faster than Python
- Why stop at C-level performance?
Additional Optimization Layers:
- Parallel loops: 366x speedup
- Parallel divide and conquer
- Vectorization
- Chip-specific features
The New Reality in 2025
- Moore's Law's automatic performance gains are gone
- LLMs make code generation easier but not necessarily better
- Need experts who understand performance optimization
- Pushing for "faster than C" as the new standard
Future Directions
- Modern compiled languages gaining attention (Rust, Go, Zig)
- Example: 16KB Zig web server in Docker
- Rethinking architectures:
- Microservices with tiny containers
- WebAssembly over JavaScript
- Performance-first design
Key Paradigm Shifts
- Developer time no longer prioritized over runtime
- Production code should never be slower than C
- Single-stack ownership enables optimization
- Need for coordinated improvement across:
- Language design
- Algorithms
- Hardware architecture
Looking Forward
- Shift from interpreted to modern compiled languages
- Performance engineering becoming critical skill
- Domain-specific hardware acceleration
- Integrated approach to performance optimization
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